activity
20202022
most citedDigital Twin Network: Opportunities and Challenges

59 citations · 68 across the 5 of their papers we have counts for

collaborators

8 papers

cs.NI2022★ 6 cited

Network Digital Twin: Context, Enabling Technologies and Opportunities

Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13

The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail…

cs.NI2022★ 59 cited

Digital Twin Network: Opportunities and Challenges

Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13

The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. Thes…

cs.DC2021

FedFly: Towards Migration in Edge-based Distributed Federated Learning

Rehmat Ullah, Di Wu, Paul Harvey +3

Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since de…

cs.DC2021★ 3 cited

FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning

Di Wu, Rehmat Ullah, Paul Harvey +3

Applying Federated Learning (FL) on Internet-of-Things devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are th…

cs.DC2021

Revisiting the Arguments for Edge Computing Research

Blesson Varghese, Eyal de Lara, Aaron Ding +8

This article argues that low latency, high bandwidth, device proliferation, sustainable digital infrastructure, and data privacy and sovereignty continue to motivate the need for e…

cs.PL2021

Multiparty Session Types for Safe Runtime Adaptation in an Actor Language (Extended version)

Paul Harvey, Simon Fowler, Ornela Dardha +1

Human fallibility, unpredictable operating environments, and the heterogeneity of hardware devices are driving the need for software to be able to adapt as seen in the Internet of…

cs.DC2020

Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks

Luke Lockhart, Paul Harvey, Pierre Imai +2

Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data,…

cs.DC2020

WattsApp: Power-Aware Container Scheduling

Hemant Mehta, Paul Harvey, Omer Rana +2

Containers are becoming a popular workload deployment mechanism in modern distributed systems. However, there are limited software-based methods (hardware-based methods are expensi…